The hardware and bandwidth for this mirror is donated by dogado GmbH, the Webhosting and Full Service-Cloud Provider. Check out our Wordpress Tutorial.
If you wish to report a bug, or if you are interested in having us mirror your free-software or open-source project, please feel free to contact us at mirror[@]dogado.de.

R build status CRAN_Status_Badge Codecov test coverage downloads

googlePolylines

A fast and light-weight implementation of Google’s polyline encoding algorithm.

Polyline encoding is a lossy compression algorithm that allows you to store a series of coordinates as a single string.

Installation

From CRAN

install.packages("googlePolylines")

From github (dev version)

remotes::install_github("SymbolixAU/googlePolylines")

Scope

Because googlePolylines uses Google’s polyline encoding algorithm, all functions assume Google Web Mercator projection (WSG 84 / EPSG:3857 / EPSG:900913) for inputs and outputs. Objects that use other projections should be re-projected into EPSG:3857 before using these functions.

googlePolylines supports Simple Feature objects (from library(sf)), data.frames, and vectors of lon/lat coordinates.

Supported sf types

Examples

googlePolylines contains functions to encode coordinates into polylines, and also to parse polylines to and from well-known text format.

encode

library(googlePolylines)
library(sf)

# create data

df <- data.frame(myId = c(1,1,1,1,1,1,1,1,2,2,2,2),
                lineId = c(1,1,1,1,2,2,2,2,1,1,1,2),
                lon = c(-80.190, -66.118, -64.757, -80.190,  -70.579, -67.514, -66.668, -70.579, -70, -49, -51, -70),
                lat = c(26.774, 18.466, 32.321, 26.774, 28.745, 29.570, 27.339, 28.745, 22, 23, 22, 22))

p1 <- as.matrix(df[1:4, c("lon", "lat")])
p2 <- as.matrix(df[5:8, c("lon", "lat")])
p3 <- as.matrix(df[9:12, c("lon", "lat")])

# create `sf` collections

point <- sf::st_sfc(sf::st_point(x = c(df[1,"lon"], df[1,"lat"])))
multipoint <- sf::st_sfc(sf::st_multipoint(x = as.matrix(df[1:2, c("lon", "lat")])))
polygon <- sf::st_sfc(sf::st_polygon(x = list(p1, p2)))
linestring <- sf::st_sfc(sf::st_linestring(p3))
multilinestring <- sf::st_sfc(sf::st_multilinestring(list(p1, p2)))
multipolygon <- sf::st_sfc(sf::st_multipolygon(x = list(list(p1, p2), list(p3))))

# combine all types into one collection

sf <- rbind(
    sf::st_sf(geo = polygon),
    sf::st_sf(geo = multilinestring),
    sf::st_sf(geo = linestring),
    sf::st_sf(geo = point),
    sf::st_sf(geo = multipoint)
    )

sf

# Simple feature collection with 5 features and 0 fields
# geometry type:  GEOMETRY
# dimension:      XY
# bbox:           xmin: -80.19 ymin: 18.466 xmax: -49 ymax: 32.321
# epsg (SRID):    NA
# proj4string:    NA
#                              geo
# 1 POLYGON ((-80.19 26.774, -6...
# 2 MULTILINESTRING ((-80.19 26...
# 3 LINESTRING (-70 22, -49 23,...
# 4          POINT (-80.19 26.774)
# 5 MULTIPOINT (-80.19 26.774, ...

# encode sf objects

encode(sf)

                                       geo
# 1         POLYGON: ohlbDnbmhN~suq@am{tA...
# 2 MULTILINESTRING: ohlbDnbmhN~suq@am{tA...
# 3      LINESTRING: _{geC~zfjL_ibE_qd_C~...
# 4                     POINT: ohlbDnbmhN...
# 5                MULTIPOINT: ohlbDnbmhN...


# encode data frame as a list of points

encode(df)
# [1] "ohlbDnbmhN~suq@am{tAw`qsAeyhGvkz`@fge}Aw}_Kycty@gc`DesuQvvrLofdDorqGtzzVfkdh@uapB_ibE_qd_C~hbE~reK?~|}rB"

Polyline to well-known text


enc <- encode(sf)
wkt <- polyline_wkt(enc)
wkt
                                                                                                                                       geo
# 1 POLYGON ((-80.19 26.774, -66.1...
# 2 MULTILINESTRING ((-80.19 26.77...
# 3 LINESTRING (-70 22, -49 23, -5...
# 4          POINT (-80.19 26.774)...
# 5 MULTIPOINT ((-80.19 26.774),(-...

Well-known text to polyline

enc2 <- wkt_polyline(wkt)

Motivation

Encoding coordinates into polylines reduces the size of objects and can increase the speed in plotting Google Maps and Mapdeck

library(sf)
library(geojsonsf)
sf <- geojsonsf::geojson_sf("https://raw.githubusercontent.com/SymbolixAU/data/master/geojson/SA1_2016_VIC.json")

encoded <- encode(sf, FALSE)
encodedLite <- encode(sf, TRUE)

vapply(mget(c('sf', 'encoded', 'encodedLite') ), function(x) { format(object.size(x), units = "Kb") }, '')
#           sf      encoded  encodedLite 
# "38750.7 Kb" "14707.9 Kb"  "9649.8 Kb"
library(microbenchmark)
library(sf)
library(geojsonsf)
library(leaflet)
library(googleway)
library(mapdeck)

sf <- geojsonsf::geojson_sf("https://raw.githubusercontent.com/SymbolixAU/data/master/geojson/SA1_2016_VIC.json")

microbenchmark(

  google = {

    ## you need a Google Map API key to use this function
    google_map(key = mapKey) %>%
      add_polygons(data = sf)
  },
  
  mapdeck = {
    mapdeck(token = mapKey) %>%
      add_polygon(data = sf)
  },

  leaflet = {
    leaflet(sf) %>%
      addTiles() %>%
      addPolygons()
  },
  times = 25
)

# Unit: milliseconds
#     expr       min        lq      mean    median        uq       max neval
#   google  530.4193  578.3035  644.9472  606.3328  726.4577  897.9064    25
#  mapdeck  527.7255  577.2322  628.5800  600.7449  682.2697  792.8950    25
#  leaflet 3247.3318 3445.6265 3554.7433 3521.6720 3654.1177 4109.6708    25

These benchmarks don’t account for the time taken for the browswer to render the maps

These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.
Health stats visible at Monitor.